Google Summer of Code Projects

Flux usually takes part in Google Summer of Code, as part of the wider Julia organisation. We follow the same rules and application guidelines as Julia, so please check there for more information on applying. Below are a set of ideas for potential projects (though you are welcome to explore anything you are interested in).

Flux projects are typically very competitive; we encourage you to get started early, as successful students typically have early PRs or working prototypes as part of the application. It is a good idea to simply start contributing via issue discussion and PRs and let a project grow from there; you can take a look at this list of issues for some starter contributions.

CUDA Hacking

Are you a performance nut? Help us implement cutting-edge CUDA kernels in Julia for operations important across deep learning, scientific computing and more. We also need help developing our wrappers for machine learning, sparse matrices and more, as well as CI and infrastructure. Contact us to develop a project plan.

Mentors: Tim Besard, Mike Innes.

Reinforcement Learning Environments

Develop a series of reinforcement learning environments, in the spirit of the OpenAI Gym. Although we have wrappers for the gym available, it is hard to install (due to the Python dependency) and, since it’s written in Python and C code, we can’t do more interesting things with it (such as differentiate through the environments). A pure-Julia version that supports a similar API and visualisation options would be valuable to anyone doing RL with Flux.

Mentors: Dhairya Gandhi, Elliot Saba.

Port ML Tutorials

There are many high-quality open-source tutorials and learning materials available, for example from PyTorch and fast.ai. We’d like to have Flux ports of these that we can add to the model zoo, and eventually publish to the Flux website.

Mentors: Dhairya Gandhi, Elliot Saba.

Model Zoo Examples

Flux’s model zoo contains examples of a wide range of deep learning models and techniques. This project would involve adding new models, showing how to recreate state-of-the-art results (e.g. AlphaGo) or interesting and unusual model architectures (e.g. transformer networks). We’d be particularly interested in any models involving reinforcement learning, or anything with images, sound or speech.

Some experience with implementing deep learning models would be ideal for this project, but is not essential for a student willing to pick up the skills and read ML papers. It’s up to you whether you implement a single ambitious model, or multiple small ones. A good source of inspiration might be the NIPS Challenge.

Note that this project is quite popular; students who show skills and interests in other parts of the stack may have an easier time distinguishing themselves.

Mentors: Dhairya Gandhi, Elliot Saba.

Deep Learning for 3D Computer Vision

Build deep learning models for 3D computer vision using Flux. There has been a lot of interest in exploiting 3D models (in the form of Voxels, Point Clouds, Meshes, etc.) for developing more reliable computer vision models. The objective of this project would be to develop a framework (powered by Flux + Zygote) which helps accelerate 3D Computer Vision research in Julia.

Some inspiration could be drawn from python frameworks like Kaolin, Pytorch3D, and Tensorflow Graphics. This project would involve developing (a few of) the following modules:

Recommended Skills: Should be familiar with 2D Computer Vision, but knowledge of 3D vision would be preferable. Some background with computer graphics would be desirable.

Expected Outcome: A 3D Computer Vision Framework for future research using Flux.

Mentors: Avik Pal, Elliot Saba

Model Zoo on TPU

Julia has experimental support for executing code on TPUs (https://github.com/JuliaTPU/XLA.jl) TPUs enable training cutting edge machine larning models written using Flux. However, TPUs are not able to execute arbitrary code and thus often require individual attention to fix new patterns in XLA.jl or other packages. Additionally, the performance characteristics of the TPU hardware are quite unlike that of CPU or even GPU and models may thus require TPU-specific adjustments to achieve peak performance. Lastly, the speed of TPUs presents signficant challenges to data input pipelines even at single-TPU levels of performance. Having a wide set of models available that are tuned for TPU will aid in finding common abstractions for models independent of hardware.

Mentors: Keno Fischer

Benchmarks

A benchmark suite would help us to keep Julia’s performance for ML models in shape, as well as revealing opportunities for improvement. Like the model-zoo project, this would involve contributing standard models that exercise common ML use case (images, text etc) and profiling them. The project could extend to include improving performance where possible, or creating a “benchmarking CI” like Julia’s own nanosoldier.

Mentors: Dhairya Gandhi, Elliot Saba.

Multi-GPU training

Implement and demonstrate multi-GPU parallelism. One route is to expose communication primitives from NVIDIA’s NCCL library and use these to build tooling for model parallelism and distributed training. The project should demonstrate parallel training of a Flux model with benchmarks.

Mentors: Valentin Churavy, Tim Besard

Distributed Training

Add a distributed training API to Flux, possibly inspired by PyTorch’s equivalent. Any distributed training algorithm could be used (ideally the foundations make it easy to experiment with different setups), though the easiest is likely to implement an MXNet-like parameter server. It should demonstrate training a Flux model with data distributed over multiple nodes, with benchmarks.

Mentors: Valentin Churavy, Tim Besard

Sparse GPU and ML support

While Julia supports dense GPU arrays well via CuArrays, we lack up-to-date wrappers for sparse operations. This project would involve wrapping CUDA’s sparse support, with CUSPARSE.jl as a starting point, adding them to CuArrays.jl, and perhaps demonstrating their use via a sparse machine learning model.

NLP Tools and Models

Build deep learning models for Natural Language Processing in Julia. TextAnalysis and WordTokenizers contains the basic algorithms and data structures to work with textual data in Julia. On top of that base, we want to build modern deep learning models based on recent research. The following tasks can span multiple students and projects.

It is important to note that we want practical, usable solutions to be created, not just research models. This implies that a large part of the effort will need to be in finding and using training data, and testing the models over a wide variety of domains. Pre-trained models must be available to users, who should be able to start using these without supplying their own training data.

Mentors: Avik Sengupta

Automated music generation

Neural network based models can be used for music analysis and music generation (composition). A suite of tools in Julia to enable research in this area would be useful. This is a large, complex project that is suited for someone with an interest in music and machine learning. This project will need a mechanism to read music files (primarily MIDI), a way to synthesise sounds, and finally a model to learn composition. All of this is admittedly a lot of work, so the exact boundaries of the project can be flexible, but this can be an exciting project if you are interested in both music and machine learning.

Recommended Skills: Music notation, some basic music theory, MIDI format, Transformer and LSTM architectures

Resources: Music Transformer, Wave2MIDI2Wave

Mentors: Avik Sengupta

Neural networks for solving differential equations

Neural networks can be used as a method for efficiently solving difficult partial differential equations. Efficient implementations from recent papers are being explored as part of the NeuralNetDiffEq.jl package. The issue tracker contains links to papers which would be interesting new neural network based methods to implement and benchmark against classical techniques.

Recommended Skills: Background knowledge in numerical analysis and machine learning.

Expected Results: New neural network based solver methods.

Mentors: Chris Rackauckas

Accelerating optimization via machine learning with surrogate models

In many cases, when attempting to optimize a function f(p) each calculation of f is very expensive. For example, evaluating f may require solving a PDE or other applications of complex linear algebra. Thus, instead of always directly evaluating f, one can develop a surrogate model g which is approximately f by training on previous data collected from f evaluations. This technique of using a trained surrogate in place of the real function is called surrogate optimization and mixes techniques from machine learning to accelerate optimization.

Advanced techniques utilize radial basis functions and Gaussian processes in order to interpolate to new parameters to estimate f in areas which have not been sampled. Adaptive training techniques explore how to pick new areas to evaluate f to better hone in on global optima. The purpose of this project is to explore these techniques and build a package which performs surrogate optimizations.

Recommended Skills: Background knowledge of standard machine learning, statistical, or optimization techniques. Strong knowledge of numerical analysis is helpful but not required.

Expected Results: Library functions for performing surrogate optimization with tests on differential equation models.

Mentors: Chris Rackauckas

Parameter estimation for nonlinear dynamical models

Machine learning has become a popular tool for understanding data, but scientists typically understand the world through the lens of physical laws and their resulting dynamical models. These models are generally differential equations given by physical first principles, where the constants in the equations such as chemical reaction rates and planetary masses determine the overall dynamics. The inverse problem to simulation, known as parameter estimation, is the process of utilizing data to determine these model parameters.

The purpose of this project is to utilize the growing array of statistical, optimization, and machine learning tools in the Julia ecosystem to build library functions that make it easy for scientists to perform this parameter estimation with the most high-powered and robust methodologies. Possible projects include improving methods for Bayesian estimation of parameters via Stan.jl and Julia-based libraries like Turing.jl, or global optimization-based approaches. Novel techniques like classifying model outcomes via support vector machines and deep neural networks is can also be considered. Research and benchmarking to attempt to find the most robust methods will take place in this project. Additionally, the implementation of methods for estimating structure, such as topological sensitivity analysis along with performance enhancements to existing methods will be considered.

Some work in this area can be found in DiffEqParamEstim.jl and DiffEqBayes.jl. Examples can be found in the DifferentialEquations.jl documentation.

Recommended Skills: Background knowledge of standard machine learning, statistical, or optimization techniques. It’s recommended but not required that one has basic knowledge of differential equations and DifferentialEquations.jl. Using the differential equation solver to get outputs from parameters can be learned on the job, but you should already be familiar (but not necessarily an expert) with the estimation techniques you are looking to employ.

Expected Results: Library functions for performing parameter estimation and inferring properties of differential equation solutions from parameters. Notebooks containing benchmarks determining the effectiveness of various methods and classifying when specific approaches are appropriate will be developed simultaneously.

Mentors: Chris Rackauckas, Vaibhav Dixit

Integration of FEniCS.jl with dolfin-adjoint + Zygote.jl for Scientific Machine Learning

Scientific machine learning requires mixing scientific computing libraries with machine learning. This blog post highlights how the tooling of Julia is fairly advanced in this field compared to alternatives such as Python, but one area that has not been completely worked out is integration of automatic differentiation with partial differential equations. FEniCS.jl is a wrapper to the FEniCS project for finite element solutions of partial differential equations. We would like to augment the Julia wrappers to allow for integration with Julia’s automatic differentiation libraries like Zygote.jl by using dolfin-adjoint. This would require setting up this library for automatic installation for Julia users and writing adjoint passes which utilize this adjoint builder library. It would result in the first total integration between PDEs and neural networks.

Recommended Skills: A basic background in differential equations and Python. Having previous Julia knowledge is preferred but not strictly required.

Expected Results: Efficient and high-quality implementations of adjoints for Zygote.jl over FEniCS.jl functions.

Mentors: Chris Rackauckas